The impact of high-intensity interval training and moderate-intensity continuous training regimes on cardiodynamic parameters in isolated heart of normotensive and hypertensive rats
Bibliographic record
Abstract
This study aimed to assess the impact of high-intensity interval training (HIIT) vs. moderate-intensity continuous training (MIT) on cardiodynamic parameters in isolated rat heart. Wistar albino rats were randomly assigned to groups according to running protocol: sedentary control, MIT, and HIIT; spontaneous hypertensive rat (SHR) sedentary control, SHR + MIT, and SHR + HIIT. HIIT groups performed the running in 5 sprints × 45–55 m/min for 30–90 s, with 2 min of rest after each sprint, while MIT groups performed the running of 10–15 m/min for 1 h with 3 min of rest every 100 m; both protocols were implemented 5 days/week over 4 weeks with 1 week of adaptation before protocols started. Isolated rat hearts were perfused according to Langendorff technique at gradually increased coronary perfusion pressures (40–120 cmH2O). Using a sensor placed in the left ventricle, we registered maximum and minimum rate of pressure development in the left ventricle, systolic and diastolic left ventricular pressure, and heart rate. Coronary flow was measured flowmetrically. MIT was connected with cardiac depression in normotensive conditions, while HIIT leads to cardiac depression in hypertensive rats. HIIT induced more significant increase of contractile and relaxation parameters of the isolated rat heart, especially in hypertensive animals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".